Bayesian Methods in Structural Bioinformatics

. Ed(s): Hamelryck, Thomas; Mardia, Kanti V.; Ferkinghoff-Borg, Jesper

ISBN 10: 3642439888 / ISBN 13: 9783642439889

Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2014

About this Item

This edited volume is a high-profile overview of the current state of play in statistical methods applied to structural bioinformatics. With almost 100 pages of introductory material, it covers topics including protein structure prediction and simulation. Editor(s): Hamelryck, Thomas; Mardia, Kanti V.; Ferkinghoff-Borg, Jesper. Series: Statistics for Biology and Health. Num Pages: 408 pages, 30 black & white tables, biography. BIC Classification: MBGR; PBT; PDE; PHVN; PSA. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 21. Weight in Grams: 623. . 2014. Paperback. . . . .

Seller Inventory # V9783642439889

Bibliographic Details

• Title: Bayesian Methods in Structural Bioinformatics
• Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
• Publication Date: 2014
• Language: English
• ISBN 10: 3642439888
• ISBN 13: 9783642439889
• Binding: Soft cover
• Condition: New

About this title

Synopsis

This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics.

About the Author

Thomas Hamelryck is an associate professor at the Bioinformatics Center, University of Copenhagen. He completed his PhD in macromolecular crystallography at the Free University of Brussels (VUB). His research interests include the application of Bayesian machine learning methods and directional statistics to the inference of protein and RNA structure, based on sequence information or experimental data.
Kanti Mardia (Senior Research Professor, University of Leeds) is a pioneering researcher and leader in modern statistical science, and is responsible for numerous groundbreaking developments; his monographs are highly acclaimed and he has played a lasting leadership role in interdisciplinary research. His most outstanding contributions lie in directional data analysis, shape analysis, spatial statistics, multivariate analysis, and protein bioinformatics.
Jesper Ferkinghoff-Borg is an associate professor at the section for Biomedical Engineering, DTU-Electro, Technical University of Denmark (DTU), Copenhagen, where he heads the computational biophysics group. He received his PhD in theoretical physics from the Niels Bohr Institute at the University of Copenhagen.